In this U01 grant we propose a 5 year engineering development effort to advance Magnetic Particle Imaging (MPI) to replace MRI as the next-generation functional brain imaging tool for human neuroscience. MPI is a young but extremely promising technology that uses the non-linear magnetic response of ironoxide nanoparticles to localize their presence in the body. MPI directly detects the nanoparticle's magnetization rather than using secondary effects on the Magnetic Resonance relaxation times. Thus, while MPI and MRI share many technologies, the MPI method does not use the MR phenomena in any way. Our plan is to detect the activation-induced and resting-state changes in the iron-oxide concentration in the cerebral capillary network by monitoring the local iron oxide concentration (and thus local Cerebral Blood Volume, CBV). This CBV-contrast source is well- proven in animal and human fMRI studies which detect CBV changes by MRI using the same iron- oxide agents, but indirectly. But, by developing MPI as the detection modality, we show that there is a potential 10-fold increase in the contrast-to-noise ratio (CNR) of human neuronal activation. In the planning grant stage, we analyzed multiple potential MPI scanner designs for humans and settled on a Field-Free-Line geometry. We developed the simulation tools to model the performance of a human sized instrument. Additionally, we validated our modeling by building and testing a small, rodent-scale MPI detector in our lab at MGH. Using this device, we showed the first ever demonstration of functional imaging with MPI using a rodent hypercapnia model. In this U01 phase we seek the resources to construct and validate the human scale, rotating FFL-style MPI device analyzed in the planning grant. After construction, we will validate its performance in phantom studies, and also in an awake-behaving macaque model that we have previously used for fMRI studies. Finally, we will validate the performance of the device in limited human fMPI studies. By developing a new and more sensitive detection methodology for functional human imaging, we hope to add a powerful new non-invasive methods to the tools available to neuroscientists studying the human brain in health and disease.